Pathogenic bacteria sensing with tellurium dioxide/cadmium gallium sulfide-assisted surface plasmonic resonance refractive index sensor: a machine learning approach

The senor quality on pathogenic bacteria monitoring employing Tellurium Dioxide (TeO 2 )/Cadmium Gallium Sulfide (CdGa 2 S 4 )-assisted surface plasmonic resonance (SPR) refractive index (RI) sensor. This proposed design is used the Kretschmann configuration with silver (Ag) for exciting the surface plasmons. A wavelength of 633 nm is used to analyze the sensor’s performance exploiting the transfer matrix method along with angular interrogation technique. Initially, we show the optimization Ag’s thickness by analyzing the better minimum reflectance and sensitivity performances. Later, the structure optimization is also exhibited by comparing the proposed structure’s performance with others that are made-up with the defined layers. Finally, the performance analysis was investigated for different 2D materials and found that maximum attained parameters are sensitivity of 285.538 ° / R I U , quality factor of 144.152 R I U − 1 , figure of merit of 134.875 R I U − 1 and combined sensitivity factor of 133.896. These results have a significant improvement as compared with the existing work. Furthermore, a machine learning framework based on the Random Forest regression algorithm was developed to predict the sensor sensitivity and evaluate the influence of the structural parameters on the sensing performance.